ic: optimal parameter search for intersection cardinality

In spEDM: Spatial Empirical Dynamic Modeling

optimal parameter search for intersection cardinality

Description

optimal parameter search for intersection cardinality

Usage

## S4 method for signature 'sf'
ic(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
ic(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  threads = detectThreads(),
  detrend = FALSE,
  grid.coord = TRUE
)

Arguments

data	observation data.
column	name of library variable.
target	name of target variable.
E	(optional) embedding dimensions.
k	(optional) number of nearest neighbors used.
tau	(optional) step of spatial lags.
style	(optional) embedding style (0 includes current state, 1 excludes it).
lib	(optional) libraries indices (input needed: vector - spatial vector, matrix - spatial raster).
pred	(optional) predictions indices (input requirement same as lib).
dist.metric	(optional) distance metric (L1: Manhattan, L2: Euclidean).
threads	(optional) number of threads to use.
detrend	(optional) whether to remove the linear trend.
nb	(optional) neighbours list.
grid.coord	(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

References

Tao, P., Wang, Q., Shi, J., Hao, X., Liu, X., Min, B., Zhang, Y., Li, C., Cui, H., Chen, L., 2023. Detecting dynamical causality by intersection cardinal concavity. Fundamental Research.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))

ic(columbus,"hoval","crime",E = 7,k = 15:25)

spEDM documentation built on Nov. 30, 2025, 5:07 p.m.